Recently, the concept of self-supervised learning is becoming attractive, where robots autonomously annotate real world data for training. Self-supervised learning has great potential to improve the robustness of robotic systems and achieve life-long learning for robots. The main challenge in self-supervision is how to annotate the real world data automatically, and meanwhile, guarantee the accuracy of the annotations. Previous works have explored self-supervised learning for model-free grasping [1]}, pixel-wise object segmentation [2]}, and object detection with bounding boxes [3]}. However, to the best of our knowledge, no work has been proposed for directly annotating 6D object pose in a self-supervised fashion. This is mainly because accurately estimating 6D object pose and automatically evaluating the estimation is challenging.
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